propensity: A Toolkit for Calculating and Working with Propensity Scores

propensity provides tools for propensity score analysis in causal inference. Calculate propensity score weights for a variety of causal estimands, handle extreme propensity scores through trimming, truncation, and calibration, and estimate causal effects with inverse probability weighting. The package supports binary, categorical, and continuous exposures.

Weight functions

Calculate propensity score weights for different causal estimands:

wt_ate(): Average treatment effect (ATE) weights
wt_att(): Average treatment effect on the treated (ATT) weights
wt_atu(): Average treatment effect on the untreated (ATU) weights (wt_atc() is an alias)
wt_atm(): Average treatment effect for the evenly matchable (ATM) weights
wt_ato(): Average treatment effect for the overlap population (ATO) weights
wt_entropy(): Entropy balancing weights
wt_cens(): Censoring weights

Propensity score modifications

Handle extreme propensity scores before calculating weights:

ps_trim(): Trim observations with extreme propensity scores
ps_trunc(): Truncate (winsorize) extreme propensity scores
ps_calibrate(): Calibrate propensity scores to improve balance
ps_refit(): Re-estimate the propensity score model after trimming

Estimation

ipw(): Inverse probability weighted estimator with variance estimation that accounts for propensity score estimation uncertainty

PSW class

The psw() class represents propensity score weights with metadata about the estimand and modifications applied:

psw(), as_psw(), is_psw(): Create and test propensity score weights
estimand(): Query the causal estimand
is_stabilized(): Check if weights are stabilized

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